The first part is a bit more straight-forward and tackles the hot topic of Internet privacy. At its end, Maciej proposes his own fix-list to address the issue:

A right to download – You should have the right to download data that you have provided, or that has been collected by observing your behavior, in a usable electronic format.

A right to delete – You should have the right to completely remove their account and all associated personal information from any online service, whenever they want.

Limits on behavioral data – Companies should only be allowed to store behavioral data for 90 days.

The right to go offline – Any device with embedded Internet access should be required to have a physical switch that disconnects the antenna, and be able to function normally with that switch turned off.

Ban on 3rd-party ad tracking – Sites serving ads should only be able to target those ads based on two things: 1) the content of the page itself 2) information the site has about the visitor

I’m far from being an expert on this topic, but if you haven’t fully given up on privacy just yet, these seem to resonate with me and I would definitely suggest giving his talk a more thorough read.

The second part is harder to summarize and perhaps the more intriguing one. It’s a critique of founders, VCs and the tech sector, trying to explain why we, as a society, have mostly failed to capture the benefits of technological change. Rather than attempt to summarize, I’ll share a few memorable quotes:

For the rest of you, if you visit San Francisco, this is something you’re likely to find unsettling. You’ll see people living in the streets, many of them mentally ill, yelling and cursing at imaginary foes. You’ll find every public space designed to make it difficult and uncomfortable to sit down or sleep, and that people sit down and sleep anyway. You’ll see human excrement on the sidewalks, and a homeless encampment across from the city hall. You’ll find you can walk for miles and not come across a public toilet or water fountain.

If you stay in the city for any length of time, you’ll start to notice other things. Lines outside every food pantry and employment office. Racially segregated neighborhoods where poverty gets hidden away, even in the richest parts of Silicon Valley. A city bureaucracy where everything is still done on paper, slowly. A stream of constant petty crime by the destitute. Public schools that no one sends their kids to if they can find an alternative. Fundraisers for notionally public services.

You can’t even get a decent Internet connection in San Francisco.

The tech industry is not responsible for any of these problems. But it’s revealing that through forty years of unimaginable growth, and eleven years of the greatest boom times we’ve ever seen, we’ve done nothing to fix them. I say without exaggeration that the Loma Prieta earthquake in 1989 did more for San Francisco than Google, Facebook, Twitter, and all the rest of the tech companies that have put down roots in the city since.

Despite being at the center of the technology revolution, the Bay Area has somehow failed to capture its benefits.

…

Our venture capitalists have an easy answer: let the markets do the work. We’ll try crazy ideas, most of them will fail, but those few that succeed will eventually change the world.

But there’s something very fishy about California capitalism.

Investing has become the genteel occupation of our gentry, like having a country estate used to be in England. It’s a class marker and a socially acceptable way for rich techies to pass their time. Gentlemen investors decide what ideas are worth pursuing, and the people pitching to them tailor their proposals accordingly.

The companies that come out of this are no longer pursuing profit, or even revenue. Instead, the measure of their success is valuation—how much money they’ve convinced people to tell them they’re worth.

Ben argues that the key scaling challenges are driven three core component becoming much more difficult as the organization grows in size:

Communication

Common Knowledge

Decision Making

Avoiding their degradation altogether is impossible, so what we’re really trying to do is “give ground grudgingly”.- try to slow them down as much as possible using three key levers. Because they all include a trade-off of increased complexity, “giving ground grudgingly” is the right strategy here, and they should be applied with their impact on the three core components in mind.

Specialization

It is typically necessary to apply this level first, but it’s also the one with the most challenging side-effects: hand-offs, conflicting agendas, etc. The next two levers aim to mitigate these negative effects.

Org Structure

There is no perfect org design since there is no way to completely eliminate the negative side-effects of specialization. Organizational design has substantial impact on the company’s communication architecture, both internally and externally – and this is the key to effectively utilizing it, using the following steps:

Figure out what needs to be communicated – key pieces of knowledge and who needs to have it

Figure out what needs to be decided – try to minimize the number of decision makers that need to be involved in making the most frequent and critical decisions

Prioritize the most important communication and decision paths – every org design represents a trade-off…

Decide who’s going to run each group

Identify the paths that you did not optimize

Build plans for mitigating the issues identified in step 5 – typically by applying the next lever:

Process

The purpose of process is communication. It’s a formal, well-structured communication vehicle, meant to ensure that:

Communication happens

It happens with quality

The people who are already doing the work are the ones who are in the best position to design the necessary process, keeping a few best practices in mind:

Focus on the output first

Figure out how you’ll know if you are getting what you want in each step – usually via some form of measurement

Engineer accountability into the system – which organization/individual is responsible for each step. Make their performance visible.

He argues that complexity is the biggest enemy to scaling. And complexity, in turn, is driven by four different attributes of the system:

States. When there are many elements in the system and each can be in one of a large number of states, then figuring out what is going on and what you should do about it grows impossible.

Interdependencies. When each element in the system can affect each other element in unpredictable ways, it’s easy to induce harmonics and other non-linear responses, driving the system out of control.

Uncertainty. When outside stresses on the system are unpredictable, the system never settles down to an equilibrium.

Irreversibility. When the effects of decisions can’t be predicted and they can’t be easily undone, decisions grow prohibitively expensive.

A successful complexity-fighting strategy must focus on eliminating one of those attributes completely and figure out a way to manage the rest.

Since uncertainty is a factor of outside forces that our by definition outside of your control, it is extremely hard to design a strategy focused on it. Strategies focused on reducing the number of states are effective in some cases (Henry Ford’s Mass Production is a notable example). But in complex systems, like software, managing states and predicting interdependencies is incredibly difficult. So instead Facebook chose to focus on eliminating irreversibility in almost everything that they do with their software: for introducing “feature switches”, through doing gradual deployments to having backup internal communication channels in case the site crashes.

A rather compelling case for auditing the full spectrum of decisions your organization makes, identifying the ones that are currently irreversible, and figuring out what it would take to make them reversible.

Last week I covered the tension between efficiency and responsiveness and between coordination and motivation. I’ve hinted that I believe that the answer to the former cannot be an “either-or” solution. You actually need both.

It’s been interesting to see over the last year or so, how Steve Blank have gradually been shifting his focus from innovation in start-ups to innovation in large corporations. I’ve covered a few of Steve’s posts in this blog already. In one of his recent posts, he ties together many of the topics that he covered, into one cohesive framework. I’m sure that if you wait long enough, you’ll be able to read about it in a book, but in the meantime:

For those of you who are familiar with Steve’s work, many of the concepts mentioned in the post and in the deck will not be new. The most interesting new piece is the “Innovation Cycle” outlined in this illustration: